We propose a generalization of the rolling guidance filter (RGF) to a similarity-based clustering (SBC) algorithm which can handle general vector data. The proposed RGF-based SBC algorithm makes the similarities between data clearer than the original similarity values computed from the original data. On the basis of the similarity values, we assign cluster labels to data by an SBC algorithm. Experimental results show that the proposed algorithm achieves better clustering result than the result by the naive application of the SBC algorithm to the original similarity values. Additionally, we study the convergence of a unimodal vector dataset to its mean vector.

In this paper, we propose a new neural filter to which the features related to a given task are input, called a neural filter with features (NFF), to improve further the performance of the conventional neural filter. In order to handle the issue concerning the optimal selection of input features, we propose a framework composed of 1) manual selection of candidates for input features related to a given task and 2) training with automatically selection of the optimal input features required for achieving the given task. Experiments on the proposed framework with an application to improving the image quality of medical X-ray image sequences were performed. The experimental results demonstrated that the performance on edge-preserving smoothing of the NFF, obtained by the proposed framework, is superior to that of the conventional neural and dynamic filters.